Achievement of Remission and Low Disease Activity Definitions in Patients with Rheumatoid Arthritis in Clinical Practice: Results from the NOR-DMARD Study
Bibliographic record
Abstract
OBJECTIVE: To examine the frequency of 6 definitions for remission and 4 definitions for low disease activity (LDA) after starting a disease-modifying antirheumatic drug (DMARD) in patients with rheumatoid arthritis (RA) in clinical practice, and to study whether predictors for achieving remission after 6 months are similar for these definitions. METHODS: Remission and LDA were calculated according to the 28-joint Disease Activity Score (DAS28), the Clinical Disease Activity Index (CDAI), the Simplified Disease Activity Index (SDAI), the Routine Assessment of Patient Index Data (RAPID3), and both the American College of Rheumatology (ACR)/European League Against Rheumatism (EULAR) Boolean remission definitions 3 and 6 months after 4992 DMARD prescriptions for patients enrolled in the NOR-DMARD, a 5-center Norwegian register. Prediction of remission after 6 months was also studied. RESULTS: After 3 months, remission rates varied between definitions from 8.7% to 22.5% and for LDA from 35.5% to 42.7%, and increased slightly until 6 months of followup. DAS28 and RAPID3 gave the highest and ACR/EULAR, SDAI, and CDAI the lowest proportions for remission. Positive predictors for remission after 6 months were similar across the definitions and included lower age, male sex, short disease duration, high level of education, current nonsmoking, nonerosive disease, treatment with a biological DMARD, being DMARD-naive, good physical function, little fatigue, and LDA. CONCLUSION: In daily clinical practice, the DAS28 and RAPID3 definitions identified remission about twice as often as the ACR/EULAR Boolean, SDAI, and CDAI. Predictors of remission were similar across remission definitions. These findings provide additional evidence to follow treatment recommendations and treat RA early with a DMARD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".